ARIMA (p and q values) I am trying to do Arima forecasting, i differenced once so d=1, Im not sure what my p and q values need to be, please check screenshots of acf and pacf below:
 A: Your ACF is (more or less) exponentially decaying.
Your PACF has a clear peak at a lag of 1, and much less clear ones at lags 2 and 34.
I recommend Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman. Based on the section on ACF/PACF reading (scroll down), it looks like your data may be best described by an ARIMA(1,1,0) process.
For "real" forecasting, an automated model selection based on information criteria, as implemented in auto.arima() in the forecast package for R, is usually better (and more scalable).

EDIT: Richard Hardy makes the excellent point that one should be very careful about higher integration orders than 1. An order of 2 is similar to a quadratic trend. This is very rarely reasonable, except for very short horizons. As an illustration, here are twenty randomly simulated ARIMA(1,2,0) paths, where the AR parameter is sampled from $U[-0.5,0.5]$ and the innovations are the default $N(0,1)$ - note how often and how soon the paths go outside $\pm 100$, even after fewer than $100$ steps:

R code:
opar <- par(mai=c(.5,.5,.1,.1),mfrow=c(4,5),las=1)
    for ( ii in 1:20 ) {
        set.seed(ii)
        plot(arima.sim(model=list(ar=runif(1,-.5,.5),order=c(1,2,0)),n=100),
            xlab="",ylab="")
    }
par(opar)

